COVID-19 in three waves in a tertiary referral hospital in Belgium: a comparison of patient characteristics, management, and outcome.

PURPOSE: Few studies have compared patient characteristics, clinical management, and outcome of patients with COVID-19 between the different epidemic waves. In this study, we describe patient characteristics, treatment, and outcome of patients admitted for COVID-19 in the Antwerp University Hospital over the first three epidemic waves of 2020-2021. METHODS: Retrospective observational study of COVID-19 patients in a Belgian tertiary referral hospital. All adult patients with COVID-19, hospitalized between February 29, 2020, and June 30, 2021, were included. Standardized routine medical data was collected from patient records. Risk factors were assessed with multivariable logistic regression. RESULTS: We included 722 patients, during the first (n = 179), second (n = 347) and third (n = 194) wave. We observed the lowest disease severity at admission during the first wave, and more elderly and comorbid patients during the second wave. Throughout the subsequent waves we observed an increasing use of corticosteroids and high-flow oxygen therapy. In spite of increasing number of complications throughout the subsequent waves, mortality decreased each wave (16.6%,15.6% 11.9% in 1st, 2nd and 3rd wave respectively). C-reactive protein above 150 mg/L was predictive for the need for intensive care unit admission (odds ratio (OR) 3.77, 95% confidence interval (CI) 2.32-6.15). A Charlson comorbidity index ≥ 5 (OR 5.68, 95% CI 2.54-12.70) and interhospital transfers (OR 3.78, 95% CI 2.05-6.98) were associated with a higher mortality. CONCLUSIONS: We observed a reduction in mortality each wave, despite increasing comorbidity. Evolutions in patient management such as high-flow oxygen therapy on regular wards and corticosteroid use may explain this favorable evolution.

Impact and evolution of risk factors associated with hospitalization and mortality due to COVID-19 during the six epidemic waves in Mexico.

Objectives: This study aims to analyze and compare the main risk factors for hospitalization and deaths due to COVID-19 during the six epidemic waves from February 2020 to June 2023 in Mexico. Methods: First, a descriptive analysis of the risk factors that led to hospitalization and mortality due to COVID-19 was performed. Next, the degree of relationship of each risk factor with hospitalization and death was determined using Cramer's V coefficient. Finally, logistic regression models were applied to estimate the odds ratios of the most statistically significant risk factors for hospitalization and mortality. Results: A direct relationship between age and the possibility of hospitalization and death due to COVID-19 was found. Moreover, the comorbidities most likely to lead to hospitalization and death were pneumonia, hypertension, diabetes, obesity and CKD. It is also remarkable that the second factor of death is endotracheal intubation. Conclusion: The COVID-19 pandemic in Mexico revealed the reality of an epidemiological scenario where infectious diseases and chronic degenerative diseases coexist and interrelate.

Clinical characteristics and outcomes among hospitalised COVID-19 patients across epidemic waves in Spain: An unCoVer analysis.

BACKGROUND AND OBJECTIVES: The COVID-19 pandemic had a significant impact in population health worldwide, and particularly in people with pre-existing chronic diseases. Early risk identification and stratification is essential to reduce the impact of future outbreaks of pandemic potential. This study aimed to comprehensively examine factors associated with COVID-19 mortality across the pandemic waves in Spain. METHODS: A retrospective study analyzed the characteristics of 13,974 patients admitted to Spanish hospitals due to SARS-CoV-2 infection from 2020-01-28 to 2022-12-31. The demographic and clinical features of patients during hospitalization on each pandemic waves were analyzed. MAIN FINDINGS: The findings highlight the heterogeneity of patient characteristics, comorbidities and outcomes, across the waves. The high prevalence of cardiometabolic diseases (53.9%) among COVID-19 patients emphasizes the importance of controlling these risk factors to prevent severe COVID-19 outcomes. CONCLUSIONS: In summary, the study associate hospital mortality with factors such as advanced age and comorbidities. The decline in mortality after the 4th wave indicates potential influences like vaccination, viral adaptation, or improved treatments. Notably, dementia and cancer metastases emerge as critical factors linked to higher mortality, highlighting the importance of addressing these conditions in COVID-19 management and preparing for future challenges.

Improvement of the software for modeling the dynamics of epidemics and developing a user-friendly interface.

The challenges humanity is facing due to the Covid-19 pandemic require timely and accurate forecasting of the dynamics of various epidemics to minimize the negative consequences for public health and the economy. One can use a variety of well-known and new mathematical models, taking into account a huge number of factors. However, complex models contain a large number of unknown parameters, the values of which must be determined using a limited number of observations, e.g., the daily datasets for the accumulated number of cases. Successful experience in modeling the COVID-19 pandemic has shown that it is possible to apply the simplest SIR model, which contains 4 unknown parameters. Application of the original algorithm of the model parameter identification for the first waves of the COVID-19 pandemic in China, South Korea, Austria, Italy, Germany, France, Spain has shown its high accuracy in predicting their duration and number of diseases. To simulate different epidemic waves and take into account the incompleteness of statistical data, the generalized SIR model and algorithms for determining the values of its parameters were proposed. The interference of the previous waves, changes in testing levels, quarantine or social behavior require constant monitoring of the epidemic dynamics and performing SIR simulations as often as possible with the use of a user-friendly interface. Such tool will allow predicting the dynamics of any epidemic using the data on the number of diseases over a limited period (e.g., 14 days). It will be possible to predict the daily number of new cases for the country as a whole or for its separate region, to estimate the number of carriers of the infection and the probability of facing such a carrier, as well as to estimate the number of deaths. Results of three SIR simulations of the COVID-19 epidemic wave in Japan in the summer of 2022 are presented and discussed. The predicted accumulated and daily numbers of cases agree with the results of observations, especially for the simulation based on the datasets corresponding to the period from July 3 to July 16, 2022. A user-friendly interface also has to ensure an opportunity to compare the epidemic dynamics in different countries/regions and in different years in order to estimate the impact of vaccination levels, quarantine restrictions, social behavior, etc. on the numbers of new infections, death, and mortality rates. As example, the comparison of the COVID-19 pandemic dynamics in Japan in the summer of 2020, 2021 and 2022 is presented. The high level of vaccinations achieved in the summer of 2022 did not save Japan from a powerful pandemic wave. The daily numbers of cases were about ten times higher than in the corresponding period of 2021. Nevertheless, the death per case ratio in 2022 was much lower than in 2020.

Threshold conditions for curbing COVID-19 with a dynamic zero-case policy derived from 101 outbreaks in China.

By 31 May 2022, original/Alpha, Delta and Omicron strains induced 101 outbreaks of COVID-19 in mainland China. Most outbreaks were cleared by combining non-pharmaceutical interventions (NPIs) with vaccines, but continuous virus variations challenged the dynamic zero-case policy (DZCP), posing questions of what are the prerequisites and threshold levels for success? And what are the independent effects of vaccination in each outbreak? Using a modified classic infectious disease dynamic model and an iterative relationship for new infections per day, the effectiveness of vaccines and NPIs was deduced, from which the independent effectiveness of vaccines was derived. There was a negative correlation between vaccination coverage rates and virus transmission. For the Delta strain, a 61.8% increase in the vaccination rate (VR) reduced the control reproduction number (CRN) by about 27%. For the Omicron strain, a 20.43% increase in VR, including booster shots, reduced the CRN by 42.16%. The implementation speed of NPIs against the original/Alpha strain was faster than the virus's transmission speed, and vaccines significantly accelerated the DZCP against the Delta strain. The CRN ([Formula: see text]) during the exponential growth phase and the peak time and intensity of NPIs were key factors affecting a comprehensive theoretical threshold condition for DZCP success, illustrated by contour diagrams for the CRN under different conditions. The DZCP maintained the [Formula: see text] of 101 outbreaks below the safe threshold level, but the strength of NPIs was close to saturation especially for Omicron, and there was little room for improvement. Only by curbing the rise in the early stage and shortening the exponential growth period could clearing be achieved quickly. Strengthening China's vaccine immune barrier can improve China's ability to prevent and control epidemics and provide greater scope for the selection and adjustment of NPIs. Otherwise, there will be rapid rises in infection rates and an extremely high peak and huge pressure on the healthcare system, and a potential increase in excess mortality.

Dengue como fenómeno epidemiológico emergente: dinámica de los brotes en la provincia de Buenos Aires en 2016 Y 2020 / Dengue as an emergent epidemiological phenomenon: dynamics of the outbreaks of 2016 and 2020 in Buenos Aires province

RESUMEN INTRODUCCIÓN: El dengue constituye un problema emergente en Argentina. En la provincia de Buenos Aires se inició un primer brote en 2016, y el de 2020 registró un 82% más de casos y afectó municipios sin antecedentes previos. El objetivo de este estudio fue caracterizar la dinámica de los brotes bonaerenses de 2016 y 2020 . MÉTODOS: Se realizó un estudio descriptivo, retrospectivo y transversal. Los casos fueron registrados en el Sistema Nacional de Vigilancia de la Salud. Fueron calculadas las distribuciones de frecuencia de los casos notificados, considerando también el origen: importados o autóctonos. Las tasas de incidencia y las razones de tasas se calcularon por Región Sanitaria. La difusión de la onda epidémica para ambos brotes fue obtenida mediante el cálculo de métricas adimensionales . RESULTADOS: Ambos brotes manifestaron ondas de similar comportamiento, pero con diferente expansión temporal y velocidades de difusión que se distanciaron en el inicio y luego hacia el final, presentando el último brote una mayor incidencia pero con una tasa de propagación de menor variación . DISCUSIÓN: La investigación realizada permitió caracterizar los brotes ocurridos en 2016 y 2020, y focalizar regionalmente la incidencia del fenómeno reemergente de la infección por arbovirus dengue (principalmente DEN-1 circulante) en la provincia de Buenos Aires, con cifras de incidencia que superaron lo conocido en la historia de la enfermedad en Argentina.

ABSTRACT INTRODUCTION: Dengue is an emerging problem in Argentina. In the province of Buenos Aires, the first outbreak was in 2016, and the one occurred during 2020 caused 82% more cases and affected districts with no previous cases. The objective of this study was to characterize the outbreak dynamics in the province of Buenos Aires in 2016 and 2020 . METHODS: A descriptive, retrospective and crosssectional study was conducted. The cases were registered in the National Health Surveillance System. The frequency distributions of the reported cases were calculated, considering also the origin: imported or autochthonous. Incidence rates and rate ratios were calculated for each Health Region. The diffusion of the epidemic wave for both outbreaks was obtained by calculating dimensionless metrics . RESULTS: Both outbreaks showed waves with similar behavior, but with different temporal expansion and diffusion speeds that distanced from each other at the beginning and then towards the end. The last outbreak had a higher incidence, but a propagation rate with less variation . DISCUSSION: This research allowed to characterize the two outbreaks occurred in 2016 and 2020, and to focus regionally on the incidence of the re-emerging phenomenon of dengue arbovirus infection (mainly circulating DEN-1) in the province of Buenos Aires, with incidence figures that exceeded those known in the history of the disease in Argentina.

Clinical Profile and Risk Factors for Severe COVID-19 in Hospitalized Patients from Rio de Janeiro, Brazil: Comparison between the First and Second Pandemic Waves.

Since COVID-19 was declared a pandemic, Brazil has become one of the countries most affected by this disease. A year into the pandemic, a second wave of COVID-19 emerged, with a rapid spread of a new SARS-CoV-2 lineage of concern. Several vaccines have been granted emergency-use authorization, leading to a decrease in mortality and severe cases in many countries. However, the emergence of SARS-CoV-2 variants raises the alert for potential new waves of transmission and an increase in pathogenicity. We compared the demographic and clinical data of critically ill patients infected with COVID-19 hospitalized in Rio de Janeiro during the first and second waves between July 2020 and October 2021. In total, 106 participants were included in this study; among them, 88% had at least one comorbidity, and 37% developed severe disease. Disease severity was associated with older age, pre-existing neurological comorbidities, higher viral load, and dyspnea. Laboratory biomarkers related to white blood cells, coagulation, cellular injury, inflammation, renal, and liver injuries were significantly associated with severe COVID-19. During the second wave of the pandemic, the necessity of invasive respiratory support was higher, and more individuals with COVID-19 developed acute hepatitis, suggesting that the progression of the second wave resulted in an increase in severe cases. These results can contribute to understanding the behavior of the COVID-19 pandemic in Brazil and may be helpful in predicting disease severity, which is a pivotal for guiding clinical care, improving patient outcomes, and defining public policies.

Epidemic Waves and Exact Solutions of a Sequence of Nonlinear Differential Equations Connected to the SIR Model of Epidemics.

The SIR model of epidemic spreading can be reduced to a nonlinear differential equation with an exponential nonlinearity. This differential equation can be approximated by a sequence of nonlinear differential equations with polynomial nonlinearities. The equations from the obtained sequence are treated by the Simple Equations Method (SEsM). This allows us to obtain exact solutions to some of these equations. We discuss several of these solutions. Some (but not all) of the obtained exact solutions can be used for the description of the evolution of epidemic waves. We discuss this connection. In addition, we use two of the obtained solutions to study the evolution of two of the COVID-19 epidemic waves in Bulgaria by a comparison of the solutions with the available data for the infected individuals.

Plausible explanation for the third COVID-19 wave in India and its implications.

Recently some of us used a random-walk Monte Carlo simulation approach to study the spread of COVID-19. The calculations were reasonably successful in describing secondary and tertiary waves of infection, in countries such as the USA, India, South Africa and Serbia. However, they failed to predict the observed third wave for India. In this work we present a more complete set of simulations for India, that take into consideration two aspects that were not incorporated previously. These include the stochastic movement of an erstwhile protected fraction of the population, and the reinfection of some recovered individuals because of their exposure to a new variant of the SARS-CoV-2 virus. The extended simulations now show the third COVID-19 wave for India that was missing in the earlier calculations. They also suggest an additional fourth wave, which was indeed observed during approximately the same time period as the model prediction.

Multiple epidemic waves as the outcome of stochastic SIR epidemics with behavioral responses: a hybrid modeling approach.

In the behavioral epidemiology (BE) of infectious diseases, little theoretical effort seems to have been devoted to understand the possible effects of individuals' behavioral responses during an epidemic outbreak in small populations. To fill this gap, here we first build general, behavior implicit, SIR epidemic models including behavioral responses and set them within the framework of nonlinear feedback control theory. Second, we provide a thorough investigation of the effects of different types of agents' behavioral responses for the dynamics of hybrid stochastic SIR outbreak models. In the proposed model, the stochastic discrete dynamics of infection spread is combined with a continuous model describing the agents' delayed behavioral response. The delay reflects the memory mechanisms with which individuals enact protective behavior based on past data on the epidemic course. This results in a stochastic hybrid system with time-varying transition probabilities. To simulate such system, we extend Gillespie's classic stochastic simulation algorithm by developing analytical formulas valid for our classes of models. The algorithm is used to simulate a number of stochastic behavioral models and to classify the effects of different types of agents' behavioral responses. In particular this work focuses on the effects of the structure of the response function and of the form of the temporal distribution of such response. Among the various results, we stress the appearance of multiple, stochastic epidemic waves triggered by the delayed behavioral response of individuals.

Identifying age- and sex-specific COVID-19 mortality trends over time in six countries.

OBJECTIVES: The COVID-19 pandemic is characterized by successive waves that each developed differently over time and through space. We aim to provide an in-depth analysis of the evolution of COVID-19 mortality during 2020 and 2021 in a selection of countries. METHODS: We focus on five European countries and the United States. Using standardized and age-specific mortality rates, we address variations in COVID-19 mortality within and between countries, and demographic characteristics and seasonality patterns. RESULTS: Our results highlight periods of acceleration and deceleration in the pace of COVID-19 mortality, with substantial differences across countries. Periods of stabilization were identified during summer (especially in 2020) among the European countries analyzed but not in the United States. The latter stands out as the study population with the highest COVID-19 mortality at young ages. In general, COVID-19 mortality is highest at old ages, particularly during winter. Compared with women, men have higher COVID-19 mortality rates at most ages and in most seasons. CONCLUSION: There is seasonality in COVID-19 mortality for both sexes at all ages, characterized by higher rates during winter. In 2021, the highest COVID-19 mortality rates continued to be observed at ages 75+, despite vaccinations having targeted those ages specifically.

Spatial variations of the third and fourth COVID-19 waves in Hong Kong: A comparative study using built environment and socio-demographic characteristics.

Since the first confirmed case was reported in January 2020, Hong Kong has experienced multiple waves of COVID-19 outbreaks. Recent literature has explored the spatial patterns of disease incidence and their relationships with the built environment and demographic characteristics. Nonetheless, few studies aim at the comparative patterns of different epidemic waves occurring in the same spatial context. This study analyses spatial patterns of the third and fourth COVID-19 epidemic waves and then evaluates the spatial relationship between case incidence and built environment and socio-demographic characteristics. By collecting local-related cases, this study incorporates a two-fold analytical strategy: (1) Using rank-size distribution and log-odd ratio to depict the spatial pattern of COVID-19 incidence rates; (2) through global and local regression models, investigating incidence's associations with the urban built environment and socio-demographic characteristics. The results reveal that the two different epidemic waves have far distinct spatial tendencies to their infection risk factors, reflecting location-specific associations with the built environments and socio-demographics. Collectively, we discover that the third and fourth COVID-19 waves are likely associated with residential context and urban activities, respectively. Practical implications are discussed that would be of interest to policymakers and health professionals.

The Clinical Course and Outcomes of Patients Hospitalized Due to COVID-19 during Three Pandemic Waves in Poland: A Single Center Observational Study.

Background: The first case of coronavirus disease 2019 (COVID-19) in Poland was reported on 4 March 2020. We aim to compare the clinical course and outcomes of patients hospitalized in the Hospital for Infectious Diseases in Warsaw due to COVID-19 during three pandemic waves. Materials and methods: The medical data were collected for all patients diagnosed with COVID-19 hospitalized in our hospital from 6 March 2020 till 30 November 2021. COVID-19 diagnosis was confirmed by nasopharyngeal swabs using real-time polymerase chain reaction assay (RT-PCR) or SARS-CoV-2 antigen test. COVID-19 waves were defined based on the number and dynamics of cases. Results: Altogether, 2138 patient medical records were analyzed. The majority of the cohort was male (1235/2138, 57.8%), and the median age was 65 years [IQR: 50−74 years]. Patients hospitalized during the third wave had lower oxygen saturation on admission (p < 0.001) and were more likely to receive oxygen supplementation (p < 0.001). Serious complications, including pneumothorax (p < 0.001) and thromboembolic complications (p < 0.001), intensive care unit admission (p = 0.034), and death (p = 0.003), occurred more often in patients of the third wave. Conclusions: During the third wave, patients in our cohort experienced a more severe course of the disease and poorer outcomes.

[Comparative study of patients hospitalized for SARS-CoV-2 infection in two consecutive waves in Tunisia]. / Étude comparative de patients hospitalisés pour une infection SARS-CoV-2 au cours de deux vagues consécutives en Tunisie.

Introduction: Since December 2019, a novel coronavirus (SARS-CoV-2) has triggered a global pandemic with a heavy medical and societal-economic toll. The health consequences were not similar during the successive waves that affected several countries. The aim of our study was to compare the sociodemographic, clinical and evolutionary features of COVID-19 patients hospitalized at the Military Hospital of Tunis (HMPIT) during the 2nd and 3rd waves that affected the country. Patients and methods: Observational prospective study involving 1,527 COVID-19 patients hospitalized at HMPIT over 11 months, divided into two periods: from July 2020 to December 2020 called the second wave (V2) and from January 2021 to May 2021 called the third wave (V3). We compared the epidemiological data, the clinical form and the evolution of the patients for each period. Results: The number of hospitalized patients was 636 during V2 compared to 891 during V3. Average age was 63.5 ± 15.3 years during V2 versus 65.8 ± 17.8 years during V3 (P = not significant [NS]). The percentage of young adults [18-40 years] was 6.5% during V2 compared to 6.7% during V3 (P = NS). The gender ratio (M/F) was 1.59 for V2 and 1.42 for V3 (P = NS). Comorbidities were present in 65% of V2 patients and 66.3% of V3 patients (P = NS), with hypertension being the most prevalent one in both groups (47.2% for V2 versus 44.9% for V3; P = NS), followed by overweight, dyslipidemia and diabetes (33% for V2 versus 39.3% for V3; P = 0.012). The median duration between symptoms onset and hospitalization was 7 days [5-10] during V2 versus 8.5 days during V3 [5-12] (P = 0.0004). The severe clinical form was present in 49% of patients admitted during V2 compared to 34.8% during V3 (P < 10-3). The critical form represented 18.6% of cases during V2 against 16.8% during V3 (P = NS). The average hospital length of stay in COVID units (outside of intensive care unit) was 8.4 ± 5.4 days during V2 and 9.8 ± 5.7 days during V3. The average length of stay was significantly longer for the intensive care unit (11.3 ± 3.4 days for V2 versus 13.8 ± 3.9 days for V3; P = 0.01). The case fatality rate was 24.5% during V2 and 20.7% during V3 (P = NS). Median age of death was 70.2 years [42-88] during V2 and 70.4 years [22-96] during V3 with 2 patients less than 40 years of age (1%) for the latter period. The gender ratio (M/F) of deceased patients was 3.21 for V2 and 1.5 for V3 (P = 0.001). The case fatality rate was higher in the intensive care unit (65.4% for V2 versus 69.7% for V3; P = NS). Causes of death were dominated by ARDS (acute respiratory distress syndrome) for both periods (55.1% for V2 versus 70.8% for V3; P = 0.002), followed by septic shock (12.8% for V2 versus 10.8% for V3; P = NS) and multi-organ failure (9.6% for V2 versus 7.0% for V3; P = NS). Conclusion: This study revealed a decrease in severe and critical clinical forms during the 3rd wave, as well as a decrease in the case fatality rate compared to the previous wave, due to improved management and vaccination. On the other hand, the percentage of ARDS was significantly higher during this wave probably related to the beginning of circulation in our country of the Delta variant causing more severe clinical cases.

The impact of a power law-induced memory effect on the SARS-CoV-2 transmission.

It is well established that COVID-19 incidence data follows some power law growth pattern. Therefore, it is natural to believe that the COVID-19 transmission process follows some power law. However, we found no existing model on COVID-19 with a power law effect only in the disease transmission process. Inevitably, it is not clear how this power law effect in disease transmission can influence multiple COVID-19 waves in a location. In this context, we developed a completely new COVID-19 model where a force of infection function in disease transmission follows some power law. Furthermore, different realistic epidemiological scenarios like imperfect social distancing among home-quarantined individuals, disease awareness, vaccination, treatment, and possible reinfection of the recovered population are also considered in the model. Applying some recent techniques, we showed that the proposed system converted to a COVID-19 model with fractional order disease transmission, where order of the fractional derivative ( α ) in the force of infection function represents the memory effect in disease transmission. We studied some mathematical properties of this newly formulated model and determined the basic reproduction number ( R 0 ). Furthermore, we estimated several epidemiological parameters of the newly developed fractional order model (including memory index α ) by fitting the model to the daily reported COVID-19 cases from Russia, South Africa, UK, and USA, respectively, for the time period March 01, 2020, till December 01, 2021. Variance-based Sobol's global sensitivity analysis technique is used to measure the effect of different important model parameters (including α ) on the number of COVID-19 waves in a location ( W C ). Our findings suggest that α along with the average transmission rate of the undetected (symptomatic and asymptomatic) cases in the community ( ß 1 ) are mainly influencing multiple COVID-19 waves in those four locations. Numerically, we identified the regions in the parameter space of α and ß 1 for which multiple COVID-19 waves are occurring in those four locations. Furthermore, our findings suggested that increasing memory effect in disease transmission ( α → 0) may decrease the possibility of multiple COVID-19 waves and as well as reduce the severity of disease transmission in those four locations. Based on all the results, we try to identify a few non-pharmaceutical control strategies that may reduce the risk of further SARS-CoV-2 waves in Russia, South Africa, UK, and USA, respectively.

The Negative Impact of COVID-19 in HCV, HIV, and HPV Surveillance Programs During the Different Pandemic Waves.

Background: The coronavirus disease 2019 (COVID-19) pandemic has been a worldwide stress test for health systems. 2 years have elapsed since the description of the first cases of pneumonia of unknown origin. This study quantifies the impact of COVID-19 in the screening program of chronic viral infections such as human papillomavirus (HPV), human immunodeficiency virus (HIV), and hepatitis C virus (HCV) along the six different pandemic waves in our population. Each wave had particular epidemiological, biological, or clinical patterns. Methods: We analyzed the number of samples for screening of these viruses from March 2020 to February 2022, the new infections detected in the pandemic period compared to the previous year, the time elapsed between diagnosis and linking to treatment and follow-up of patients, and the percentage of late HIV diagnosis. Moreover, we used the origin of the samples as a marker for quantifying the restoration of activity in primary care. Results: During the first pandemic year, the number of samples received was reduced by 26.7, 22.6, and 22.5% for molecular detection of HPV or serological HCV and HIV status respectively. The highest decrease was observed during the first wave with 70, 40, and 26.7% for HPV, HCV, and HIV. As expected, new diagnoses also decreased by 35.4, 58.2, and 40.5% for HPV, HCV, and HIV respectively during the first year of the pandemic. In the second year of the pandemic, the number of samples remained below pre-pandemic period levels for HCV (-3.6%) and HIV (-9.3%) but was slightly higher for HPV (8.0%). The new diagnoses in the second year of the pandemic were -16.1, -46.8, and -18.6% for HPV, HCV, and HIV respectively. Conclusions: Undoubtedly, an important number of new HPV, HCV, and HIV infections were lost during the COVID-19 pandemic, and surveillance programs were disrupted as a consequence of collapse of the health system. It is a priority to reinforce these surveillance programs as soon as possible in order to detect undiagnosed cases before the associated morbidity-mortality increases. New pandemic waves could increase the risk of reversing the achievements made over the last few decades.

Covid-19, an unfinished story.

The Covid-19 pandemic appeared in China in December 2019 as a cluster of transmissible pneumonia caused by a new betacoronavirus. On March 11, 2020, the World Health Organization (WHO) declared it a pandemic. Covid-19 is a mild infection in 80% of cases, serious in 15% and critical in 5%. Symptomatic forms include a first phase of flu-like viral invasion, and at times a second phase, dysimmune and inflammatory, with acute respiratory distress syndrome, multiorgan failure and thromboembolic complications. Degree of severity is related to age and comorbidities. SARS-CoV-2 is the third highly pathogenic Betacoronavirus to cross the species barrier. Its genome, an RNA of 29,903 nucleotides, shows strong homogeneity with bat coronaviruses from southern China, but the conditions for its passage in humans have yet to be elucidated. Mutations can give rise to variants of concern (VOC) that are more transmissible and able to evade the host's immune response. Several VOCs have succeeded and replaced one another: Alpha in October 2020, Beta and Gamma in December 2020, Delta in spring 2021 and Omicron in November 2021. The Covid-19 pandemic has evolved in five waves of unequal amplitude and severity, with geographical disparities. Worldwide, it has caused 395,000,000 confirmed cases including 5,700,000 deaths. Epidemiological surveillance applies several indicators (incidence rate, test positivity rate, effective R and occupancy rate of intensive care beds) supplemented by genomic monitoring to detect variants by sequencing. Non-pharmacological measures, particularly face mask wearing, have been effective in preventing the transmission of SARS-CoV-2. Few currently available drugs have proven useful, with the exception of dexamethazone for patients requiring oxygen therapy. Development of SARS-CoV-2 vaccines began early on many platforms. Innovation was brought about by the Pfizer-BioNTech and Moderna messenger RNA vaccines, which claim protective efficacy of 95% and 94.1% respectively, far higher than the 70% minimum set by the WHO. Governments have hesitated between two strategies, mitigation and suppression. The second has been favored in critical periods such as April 2020, when 2.5 billion people throughout the world were confined. Vaccination campaigns got underway at the end of December 2020 and progressed without reaching sufficient herd immunity, leading some nations to consider compulsory vaccination or to require a vaccine or health pass, in order for persons to access different activities. Will the pandemic stop with Omicron and become endemic? This part of the Covid-19 story remains to be told.

Evolution of SARS-CoV-2 in Spain during the First Two Years of the Pandemic: Circulating Variants, Amino Acid Conservation, and Genetic Variability in Structural, Non-Structural, and Accessory Proteins.

Monitoring SARS-CoV-2's genetic diversity and emerging mutations in this ongoing pandemic is crucial to understanding its evolution and ensuring the performance of COVID-19 diagnostic tests, vaccines, and therapies. Spain has been one of the main epicenters of COVID-19, reaching the highest number of cases and deaths per 100,000 population in Europe at the beginning of the pandemic. This study aims to investigate the epidemiology of SARS-CoV-2 in Spain and its 18 Autonomous Communities across the six epidemic waves established from February 2020 to January 2022. We report on the circulating SARS-CoV-2 variants in each epidemic wave and Spanish region and analyze the mutation frequency, amino acid (aa) conservation, and most frequent aa changes across each structural/non-structural/accessory viral protein among the Spanish sequences deposited in the GISAID database during the study period. The overall SARS-CoV-2 mutation frequency was 1.24 × 10−5. The aa conservation was >99% in the three types of protein, being non-structural the most conserved. Accessory proteins had more variable positions, while structural proteins presented more aa changes per sequence. Six main lineages spread successfully in Spain from 2020 to 2022. The presented data provide an insight into the SARS-CoV-2 circulation and genetic variability in Spain during the first two years of the pandemic.

A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen-Shannon Divergence.

A novel yet simple extension of the symmetric logistic distribution is proposed by introducing a skewness parameter. It is shown how the three parameters of the ensuing skew logistic distribution may be estimated using maximum likelihood. The skew logistic distribution is then extended to the skew bi-logistic distribution to allow the modelling of multiple waves in epidemic time series data. The proposed skew-logistic model is validated on COVID-19 data from the UK, and is evaluated for goodness-of-fit against the logistic and normal distributions using the recently formulated empirical survival Jensen-Shannon divergence (ESJS) and the Kolmogorov-Smirnov two-sample test statistic (KS2). We employ 95% bootstrap confidence intervals to assess the improvement in goodness-of-fit of the skew logistic distribution over the other distributions. The obtained confidence intervals for the ESJS are narrower than those for the KS2 on using this dataset, implying that the ESJS is more powerful than the KS2.

Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach.

Phenomenological and deterministic models are often used for the estimation of transmission parameters in an epidemic and for the prediction of its growth trajectory. Such analyses are usually based on single peak outbreak dynamics. In light of the present COVID-19 pandemic, there is a pressing need to better understand observed epidemic growth with multiple peak structures, preferably using first-principles methods. Along the lines of our previous work  [Physica A 574, 126014 (2021)], here we apply 2D random-walk Monte Carlo calculations to better understand COVID-19 spread through contact interactions. Lockdown scenarios and all other control interventions are imposed through mobility restrictions and a regulation of the infection rate within the stochastically interacting population. The susceptible, infected and recovered populations are tracked over time, with daily infection rates obtained without recourse to the solution of differential equations. The simulations were carried out for population densities corresponding to four countries, India, Serbia, South Africa and USA. In all cases our results capture the observed infection growth rates. More importantly, the simulation model is shown to predict secondary and tertiary waves of infections with reasonable accuracy. This predictive nature of multiple wave structures provides a simple and effective tool that may be useful in planning mitigation strategies during the present pandemic.